class_docstr: Covariance metric (baseline method)
  
  This method does not "learn" anything, rather it calculates
  the covariance matrix of the input data.
  
  This is a simple baseline method first introduced in
  On the Generalized Distance in Statistics, P.C.Mahalanobis, 1936
  
  Read more in the :ref:`User Guide <covariance>`.
  
  Attributes
  ----------
  components_ : `numpy.ndarray`, shape=(n_features, n_features)
      The linear transformation ``L`` deduced from the learned Mahalanobis
      metric (See function `components_from_metric`.)
  
  Examples
  --------
  >>> from metric_learn import Covariance
  >>> from sklearn.datasets import load_iris
  >>> iris = load_iris()['data']
  >>> cov = Covariance().fit(iris)
  >>> x = cov.transform(iris)
code: def __init__(self, preprocessor=None):
      super(Covariance, self).__init__(preprocessor)
  
file_path: metric-learn/metric_learn/covariance.py
imports: import numpy
  import scipy
  from sklearn.base import TransformerMixin
  from .base_metric import MahalanobisMixin
  from ._util import components_from_metric
label_desc: Use image node_id 1 to create a new Covariance object from inherited base classes: MahalanobisMixin, TransformerMixin with example: obj = Covariance(preprocessor)